Power grid state visualization and operation and maintenance management method, device, equipment and medium

Abstract

The invention relates to the technical field of text analysis of embedded equipment. The power grid state visualization and operation and maintenance management method comprises the steps that operation state information is input into an equipment state analysis model to obtain equipment health state information, the equipment health state information is displayed in a power grid state display interface, and if the equipment health state information is abnormal, the power grid state is displayed. If yes, generating equipment repair information, obtaining recovered equipment operation state information based on the equipment repair information, performing trend prediction analysis on the recovered equipment operation state information through an equipment state trend prediction model to obtain a trend prediction analysis result, and generating predictive operation and maintenance task information according to the trend prediction analysis result. And generating task scheduling information according to the predictive operation and maintenance task information. The method has the effect of improving the intelligent level of power grid state visual monitoring and operation and maintenance management.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to a method, device, equipment and medium for visualizing and operating and maintaining power grid status. Background Art

[0002] As power grids continue to expand in scale and intelligence, the demand for monitoring the operating status and troubleshooting of power equipment continues to grow. Existing technologies typically install monitoring terminals to regularly collect equipment operating status information and perform preliminary analysis of equipment health based on set thresholds or manual experience. Some systems also provide a grid status visualization interface to display the current operating status of equipment, helping operators identify equipment anomalies.

[0003] However, under the existing O&M system, equipment status analysis is largely based on static data, lacking in-depth predictions of equipment health trends over time. This makes it difficult to promptly identify potential failure risks. After equipment anomaly detection and repair, there is often a lack of continuous tracking and trend review of the repair results, making it impossible to verify O&M effectiveness in a closed-loop manner. Furthermore, the generation and scheduling of existing O&M tasks primarily relies on manual judgment, failing to intelligently optimize allocation based on the real-time location of O&M personnel, task priorities, and resource usage. This results in low overall response speed and O&M efficiency. Summary of the Invention

[0004] In order to improve the intelligence level of power grid status visualization monitoring and operation and maintenance management, the present application provides a power grid status visualization and operation and maintenance management method, device, equipment and medium.

[0005] The above-mentioned invention objective of this application is achieved through the following technical solutions: A method for visualizing and managing power grid status, comprising: Obtain operating status information of power equipment in the power grid; Inputting the operating status information into the equipment status analysis model to obtain equipment health status information; Displaying the health status information of the device in the power grid status display interface; If the device health status information is abnormal, device repair information is generated, and the target device is repaired based on the device repair information to obtain restored device operation status information; Performing trend prediction analysis on the post-recovery equipment operation status information using an equipment status trend prediction model to obtain a trend prediction analysis result; Based on the trend prediction analysis results, determining the health status change trend of the target device within a set evaluation time interval, and generating predictive operation and maintenance task information if the health status change trend indicates that the target device is at risk; Task scheduling information is generated based on the predictive operation and maintenance task information, combined with the real-time location information, task priority, resource occupancy, and response rules of the operation and maintenance personnel.

[0006] By adopting the above technical solution, real-time collection of power equipment operating status information and intelligent analysis of equipment health status are realized. Trend prediction can be carried out based on the equipment operating status information after restoration, potential risks of power equipment can be identified in advance, and the timeliness and accuracy of power equipment anomaly detection can be improved. It supports dynamic tracking of changes in the operating status of restored equipment after the completion of power equipment repair, establishes a closed-loop verification process for equipment health management, and further combines the real-time location information of operation and maintenance personnel, the priority information of operation and maintenance tasks, resource occupancy information and preset response rules to generate optimized task scheduling information, realize intelligent allocation and dynamic adjustment of operation and maintenance tasks, effectively improve the overall operation and maintenance response speed and operating efficiency of the power grid, and overcome the problems of risk identification lag caused by static data analysis, lack of health tracking and verification after equipment repair, and low efficiency of operation and maintenance scheduling due to reliance on manual decision-making in existing technologies.

[0007] In a preferred example, the present application may be further configured as follows: inputting the operating status information into the device status analysis model to obtain device health status information includes: Performing feature extraction processing on the received operating status information through the device status analysis model to extract device operating status indicator information from the operating status information; The device status analysis model performs normalization processing on the device operation status indicator information based on a preset feature weight factor to obtain normalized device operation status indicator information; The equipment status analysis model performs weighted summation processing on the standardized equipment operation status indicator information based on the characteristic weight ratio corresponding to each indicator to obtain a preliminary health score value; The preliminary health score value is compared with the health assessment standard corresponding to the device category to determine the device health status information.

[0008] By adopting the above technical solution, the key features in the operating status information of power equipment are effectively extracted and standardized. The operating status indicators of different equipment can be reasonably normalized according to the set feature weight factors. A weighted summation calculation is performed based on the standardized equipment operating status indicator information to generate a preliminary health score value reflecting the overall operating health level of the equipment. By comparing the preliminary health score value with the health assessment standard corresponding to the equipment category, the current health status of the power equipment can be accurately determined, effectively improving the objectivity and accuracy of the health status analysis of the power equipment.

[0009] In a preferred example, the present application may be further configured as follows: performing trend prediction analysis on the post-recovery equipment operation status information using the equipment status trend prediction model to obtain trend prediction analysis results, including: Performing trend analysis on the post-recovery equipment operating status information through an equipment status trend prediction model to extract health trend characteristic parameters; Based on the health trend characteristic parameters, a trend prediction analysis result is generated.

[0010] By adopting the above technical solution, it is possible to perform trend analysis based on the equipment operating status information after recovery, extract characteristic parameters reflecting the trend of changes in the health of the equipment operation, generate trend prediction analysis results based on the health trend characteristic parameters, identify the risk direction and change rate of potential deterioration of the equipment health status in advance, improve the foresight and dynamic tracking capabilities of the health status assessment of power equipment, and overcome the problem that the existing technology only relies on static detection data for health assessment and cannot make in-depth predictions on equipment operation trends.

[0011] In a preferred example, the present application may be further configured as follows: performing trend analysis on the post-recovery device operating status information using the device status trend prediction model to extract health trend characteristic parameters, including: Arranging the restored device operating status information in chronological order to form a restored device operating status time series; Based on the time series of the equipment operating status after recovery, a sliding time window method is used to extract a subsequence of the equipment operating status after recovery within a preset time period; For the post-recovery device operating state subsequence, health trend change characteristics are calculated and corresponding health trend characteristic parameters are extracted.

[0012] By adopting the above technical solution, the operating status information of the restored equipment can be arranged in chronological order to form a continuous and complete equipment operating status time series. Based on the time series, the operating status subsequence within the preset time period is extracted through a sliding time window, and the characteristics of each subsequence are further analyzed. The health trend change characteristics are calculated and the health trend characteristic parameters are extracted. In this way, the health status change trend of the power equipment can be modeled and predicted based on continuous time series data, effectively breaking through the potential fault risk identification lag problem caused by relying on static data analysis in the existing technology. At the same time, after the equipment is repaired, the operating status change trajectory of the restored equipment can be continuously tracked.

[0013] In a preferred example, the present application may be further configured as follows: determining the health status change trend of the target device within a set evaluation time interval based on the trend prediction analysis result, including: Based on the trend prediction analysis results, calculate the trend comprehensive change index of the target device within the set evaluation time interval Acquire device attribute information corresponding to the target device; Dynamically adjusting trend determination information based on the device attribute information; The health status change trend of the target device within the set evaluation time interval is determined based on the trend comprehensive change index and the trend determination information.

[0014] By adopting the above technical solution, it is possible to calculate the comprehensive trend change index of the target equipment within the set evaluation time interval based on the trend prediction analysis results, and dynamically adjust the trend judgment information in combination with the equipment attribute information corresponding to the target equipment, so as to realize trend evaluation standards that are flexibly adapted to different equipment categories and operating environment characteristics. Further, based on the comprehensive trend change index and the adjusted trend judgment information, the health status change trend of the target equipment within the set evaluation time interval can be judged, thereby improving the ability to accurately identify changes in the operating trends of power equipment and timely discover potential degradation risks.

[0015] In a preferred example, the present application may be further configured as follows: dynamically adjusting the trend determination information according to the device attribute information includes: The device attribute information includes device category information and operating environment parameter information Determining basic trend determination information corresponding to the target device according to the device category information; Calculating an environmental correction factor based on the operating environment parameter information; The basic trend determination information is fused with the environmental correction factor to generate the trend determination information.

[0016] By adopting the above technical solution, the equipment category information and the operating environment parameter information can be used as the components of the equipment attribute information. The basic trend judgment information corresponding to the target equipment can be determined according to the equipment category information, and the environmental correction factor can be further calculated based on the operating environment parameter information. The basic trend judgment information and the environmental correction factor are integrated to generate trend judgment information that dynamically adapts to different equipment categories and operating environment characteristics, realizes the personalization of trend evaluation standards and environmental sensitivity adjustment, overcomes the problems of the single equipment health status evaluation standard and the lack of environmental adaptive mechanism in the existing technology, and improves the ability to accurately judge the changing trend of the operating status of power equipment.

[0017] In a preferred example, the present application may be further configured as follows: fusing the basic trend determination information with the environmental correction factor to generate the trend determination information includes: Determining correction direction information and correction amplitude information for the basic trend determination information based on the environmental correction factor; According to the correction direction information and the correction amplitude information, correction processing is performed on the basic trend determination information to obtain the trend determination information.

[0018] By adopting the above technical solution, it is possible to determine the correction direction information and correction amplitude information of the basic trend judgment information based on the environmental correction factor, perform correction processing on the basic trend judgment information according to the correction direction information and correction amplitude information, and generate trend judgment information that matches the actual operating environment and status characteristics of the equipment, thereby realizing dynamic optimization and environmental adaptive adjustment of the health trend assessment standards of power equipment, overcoming the problem in the existing technology that the equipment health assessment standards are statically fixed and cannot be flexibly adjusted according to environmental changes, and improving the accuracy and reliability of equipment operation status trend identification.

[0019] The second object of the present invention is achieved through the following technical solutions: A power grid status visualization and operation and maintenance management device, comprising: Operation status acquisition module, used to obtain operation status information of power equipment in the power grid; An equipment status analysis module is used to input the operating status information into an equipment status analysis model to obtain equipment health status information; A status information display module is used to display the health status information of the device in the power grid status display interface; an equipment abnormality processing module, configured to generate equipment repair information if the equipment health status information is abnormal, repair the target equipment based on the equipment repair information, and obtain equipment operating status information after recovery; A trend prediction analysis module is used to perform trend prediction analysis on the post-recovery equipment operation status information using an equipment status trend prediction model to obtain a trend prediction analysis result; a risk assessment and task generation module, configured to determine, based on the trend prediction analysis results, the health status change trend of the target device within a set assessment time interval, and generate predictive operation and maintenance task information if the health status change trend indicates that the target device is at risk; The operation and maintenance task scheduling module is used to generate task scheduling information based on the predictive operation and maintenance task information, combined with the real-time location information, task priority, resource occupancy and response rules of the operation and maintenance personnel.

[0020] By adopting the above technical solution, real-time collection of power equipment operating status information and intelligent analysis of equipment health status are realized. Trend prediction can be carried out based on the equipment operating status information after restoration, potential risks of power equipment can be identified in advance, and the timeliness and accuracy of power equipment anomaly detection can be improved. It supports dynamic tracking of changes in the operating status of restored equipment after the completion of power equipment repair, establishes a closed-loop verification process for equipment health management, and further combines the real-time location information of operation and maintenance personnel, the priority information of operation and maintenance tasks, resource occupancy information and preset response rules to generate optimized task scheduling information, realize intelligent allocation and dynamic adjustment of operation and maintenance tasks, effectively improve the overall operation and maintenance response speed and operating efficiency of the power grid, and overcome the problems of risk identification lag caused by static data analysis, lack of health tracking and verification after equipment repair, and low efficiency of operation and maintenance scheduling due to reliance on manual decision-making in existing technologies.

[0021] The third objective of this application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for visualizing and operating and maintaining a power grid are implemented.

[0022] The fourth objective of this application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned power grid status visualization and operation and maintenance management method.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. It realizes the real-time collection of power equipment operating status information and intelligent analysis of equipment health status. It can make trend predictions based on the equipment operating status information after restoration, identify potential risks of power equipment in advance, improve the timeliness and accuracy of power equipment anomaly detection, support dynamic tracking of the operating status changes of restored equipment after the completion of power equipment repair, establish a closed-loop verification process for equipment health management, and further combine the real-time location information of operation and maintenance personnel, the priority information of operation and maintenance tasks, resource occupancy information and preset response rules to generate optimized task scheduling information, realize intelligent allocation and dynamic adjustment of operation and maintenance tasks, effectively improve the overall operation and maintenance response speed and operating efficiency of the power grid, and overcome the problems of risk identification lag caused by static data analysis, lack of health tracking and verification after equipment repair, and low efficiency of operation and maintenance scheduling due to reliance on manual decision-making in existing technologies; 2. It can determine the correction direction and amplitude information of basic trend determination information based on the environmental correction factor, perform correction processing on the basic trend determination information according to the correction direction and amplitude information, and generate trend determination information that matches the actual operating environment and status characteristics of the equipment. This realizes dynamic optimization and environmental adaptive adjustment of the health trend assessment standard of power equipment, overcomes the problem of static and fixed equipment health assessment standards in the existing technology and the inability to flexibly adjust according to environmental changes, and improves the accuracy and reliability of equipment operating status trend identification; 3. Taking the equipment category information and operating environment parameter information as the components of the equipment attribute information, the basic trend judgment information corresponding to the target equipment is determined according to the equipment category information, and the environmental correction factor is further calculated based on the operating environment parameter information. The basic trend judgment information and the environmental correction factor are integrated to generate trend judgment information that dynamically adapts to different equipment categories and operating environment characteristics, realizes the personalization of trend evaluation standards and environmental sensitivity adjustment, overcomes the problems of single equipment health status evaluation standards and lack of environmental adaptive mechanism in the existing technology, and improves the ability to accurately judge the changing trend of the operating status of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for visualizing and managing power grid status in one embodiment of the present application.

[0025] Figure 2 This is a flowchart for implementing step S20 in a method for visualizing and operating power grid status in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S50 in a method for visualizing and operating and maintaining a power grid in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S501 in a method for visualizing and operating power grid status in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S60 in a method for visualizing and operating and maintaining a power grid in one embodiment of the present application; Figure 6 This is a flowchart for implementing step S603 in a method for visualizing and operating and maintaining a power grid in one embodiment of the present application; Figure 7 This is a flowchart for implementing step S604 in a method for visualizing and operating and maintaining a power grid in one embodiment of the present application; Figure 8 This is a principle block diagram of a power grid status visualization and operation and maintenance management device in one embodiment of the present application; Figure 9 is a schematic diagram of a device in one embodiment of the present application; DETAILED DESCRIPTION The present application is further described in detail below with reference to the accompanying drawings.

[0026] In one embodiment, if Figure 1 As shown, the present application discloses a method for visualizing and managing power grid status, which specifically includes the following steps: S10: Obtaining operating status information of power equipment in the power grid.

[0027] Specifically, the basic operating parameters of the power equipment are collected through monitoring equipment installed at key nodes of the power grid. The basic operating parameters include current value, voltage value, equipment temperature, load rate and cumulative operating time. The original signal data of each operating parameter is read at a set sampling frequency through the sensor acquisition module. The timing control logic of the on-site terminal is used to trigger the data reporting process. The read original parameters are encapsulated in a unified data format, and the equipment number and timestamp information are marked. The edge processing module performs preliminary anomaly elimination and integrity verification on the collected data, eliminates data fragments with abnormal physical characteristics, and packages the equipment operating parameters that meet the requirements into standard messages. The messages are transmitted to the data receiving end through the communication link. The receiving end archives and organizes them according to the equipment number and time sequence to obtain the operating status information of the power equipment in the power grid.

[0028] S20: Input the operating status information into the equipment status analysis model to obtain equipment health status information.

[0029] Specifically, the collected equipment operation status information is input into the equipment status analysis model, and the key operation characteristics in the equipment operation status information are extracted based on the feature extraction logic. The key operation characteristics include current amplitude characteristics, voltage stability characteristics, temperature change characteristics, load rate fluctuation characteristics and cumulative operation time characteristics. The extracted operation characteristics are standardized according to the physical dimensions of each characteristic, and the characteristics of different dimensions are uniformly mapped to a preset numerical range. Subsequently, a weighted operation is performed on the standardized feature parameters according to the preset feature weight factor, and the comprehensive contribution of each feature to the overall health level of the equipment is calculated. The preliminary health score value is obtained through a weighted sum operation, and then the preliminary health score value is compared with the health assessment benchmark value corresponding to the equipment category. The health level classification of the current equipment is determined based on the comparison results to obtain the equipment health status information.

[0030] S30: Displaying the health status information of the device in the power grid status display interface.

[0031] Specifically, the determined equipment health status information is mapped and associated with the power grid topology node according to the equipment number, and the health level identifier corresponding to each device is extracted through the interface rendering module. Different icon shapes or colors are used in the interface to distinguish different health levels. Equipment with normal health status is marked in green, and equipment with abnormal health status is marked in yellow or red. At the same time, a brief operating status information bar is attached around the node to dynamically display a summary of the key operating parameters of the current equipment. The real-time and consistency of the interface element update are guaranteed by smooth rendering technology. It supports clicking the device node in the interface to pop up a detailed equipment health assessment report window. The report content includes health score values, abnormal indicator prompts and operating trend prompts. All displayed content is automatically updated synchronously according to the set refresh frequency to obtain a visual display of the equipment health status information.

[0032] S40: If the device health status information is abnormal, device repair information is generated, and the target device is repaired based on the device repair information to obtain restored device operation status information.

[0033] Specifically, when abnormal indicators are detected in the equipment health status information, equipment repair information is generated according to the abnormal type corresponding to the abnormal indicator. The equipment repair information contains the identification information of the target equipment, the abnormal item number and the corresponding repair suggestion. The abnormal item that needs to be repaired and the repair method are determined by parsing the equipment repair information, and historical maintenance records and equipment specification data are retrieved as auxiliary basis for repair. On-site repair operations are carried out according to the preset repair operation process. During the repair process, changes in key operating parameters are monitored in real time to ensure that the repair measures are effectively implemented. After the repair is completed, the operating status parameters of the target equipment are re-collected, and the parameter changes before and after the repair are compared and analyzed to confirm that the repair effect meets the standard requirements. Finally, the collected equipment operating status parameters after repair are archived as the restored equipment operating status information to obtain the restored equipment operating status information.

[0034] S50: performing trend prediction analysis on the restored equipment operation status information using the equipment status trend prediction model to obtain trend prediction analysis results.

[0035] Specifically, the operating status information of the restored equipment is input into the equipment status trend prediction model, and the various operating status indicators are arranged in chronological order. The sliding time window method is used to extract the indicator change sequence within each preset time period, and the extracted indicator subsequence is subjected to curve fitting processing by applying the time series fitting algorithm. The change direction, change rate and change amplitude of the indicator in each time window are calculated. Based on the trend characteristic parameters extracted in each time window, the prediction rules within the trend prediction model are used to extrapolate the trend of the operating status of the target equipment within the set evaluation time interval. The trend characteristics of each indicator are comprehensively considered to determine the changing trend of the overall health status to obtain the trend prediction analysis results.

[0036] S60: Based on the trend prediction analysis results, determine the health status change trend of the target device within the set evaluation time interval. If the health status change trend shows that the target device is at risk, generate predictive operation and maintenance task information.

[0037] Specifically, based on the health trend characteristic parameters extracted from the trend prediction analysis results, the direction and rate of change of the health status of the target device within the set evaluation time interval are judged according to the set trend classification rules. By judging whether the direction of change points to a deterioration trend and the rate of change exceeds the preset sensitive threshold, it is determined that the target device has potential risks. The target device with potential risks is marked, and the corresponding device number, risk level, estimated risk occurrence time window and recommended preliminary treatment measures are extracted. Predictive operation and maintenance task information is generated based on the above information. The predictive operation and maintenance task information includes a list of equipment that needs to be prioritized, the recommended response time and the recommended operation and maintenance strategy to obtain predictive operation and maintenance task information.

[0038] S70: Generate task scheduling information based on the predictive operation and maintenance task information, combined with the real-time location information, task priority, resource usage, and response rules of the operation and maintenance personnel.

[0039] Specifically, the target device number, risk level and estimated processing time contained in the predictive operation and maintenance task information are parsed, the real-time location information of the current online operation and maintenance personnel is extracted, the distance parameters between each operation and maintenance personnel and the target device are calculated, and all pending tasks are sorted according to the urgency based on the task priority information. The current occupancy of operation and maintenance resources is collected, including the number of tasks being processed by each operation and maintenance personnel and the available working hours. The set response rules are applied to give priority to matching operation and maintenance personnel with a closer distance and available resources. Multiple qualified personnel are sorted and selected according to the response speed priority, and the selected personnel are bound to the corresponding tasks. Task scheduling data including personnel allocation, task execution start and end time and scheduling path is generated to obtain task scheduling information.

[0040] In one embodiment, if Figure 2 As shown, in step S20, the operating status information is input into the equipment status analysis model to obtain equipment health status information, including: S201: performing feature extraction processing on the received operating status information through the device status analysis model to extract device operating status indicator information from the operating status information.

[0041] Specifically, the received equipment operation status information is parsed and processed according to the set feature extraction rules to extract equipment operation status characteristics including equipment current amplitude change characteristics, voltage stability characteristics, temperature fluctuation range characteristics, load rate change trend characteristics and cumulative operation time characteristics. A corresponding numerical mapping relationship is established for each feature parameter, and the original sampling values ​​are merged into the standard feature set according to the corresponding feature category. During the feature extraction process, a sliding sampling window is used to pre-process continuous data segments to eliminate isolated data points caused by abnormal disturbances. The extracted feature parameters are encapsulated and marked with a unified data structure, indicating the source time, equipment number and feature category, so as to extract equipment operation status indicator information.

[0042] S202: The device status analysis model performs normalization processing on the device operation status indicator information based on a preset feature weight factor to obtain normalized device operation status indicator information.

[0043] Specifically, according to the physical quantity category and corresponding dimensional attributes of each equipment operation status indicator information, the maximum allowable value, minimum allowable value and normal interval reference value corresponding to the feature are extracted, and the equipment operation status indicator information is mapped to a unified standard numerical interval through normalization processing. In the standardization process, the linear scaling rule or the interval compression rule is applied according to the feature category. The linear scaling method is used to standardize the amplitude change type features, and the interval compression method is used to standardize the fluctuation range type features. The standardized feature parameters are uniformly collected, and the weight values ​​are marked according to the preset feature weight factor. The weight values ​​are allocated according to the feature importance and are associated one-to-one with the standardized feature parameters to obtain the standardized equipment operation status indicator information.

[0044] S203: The equipment status analysis model performs weighted summation processing on the standardized equipment operation status indicator information based on the feature weight ratio corresponding to each indicator to obtain a preliminary health score value.

[0045] Specifically, the standardized equipment operation status indicator information is correlated and matched according to the preset feature weight ratio, and the corresponding weight coefficient is extracted for each standardized feature parameter. According to the weighted calculation rule, the standardized feature parameter value and the weight coefficient are multiplied to obtain the weighted value corresponding to each standardized feature parameter. All weighted values ​​are merged according to the feature category and then summed up. During the summation process, the weighted results of high-weight features are superimposed first according to the feature importance, and the weighted results of low-weight features are adjusted proportionally and superimposed. After summing, the preliminary health score value corresponding to the target equipment is obtained. The preliminary health score value reflects the comprehensive evaluation result of the overall health level of the equipment to obtain the preliminary health score value.

[0046] S204: Compare the preliminary health score value with the health assessment standard corresponding to the device category to determine the device health status information.

[0047] Specifically, the health assessment standards corresponding to the target device category are extracted. The health assessment standards include scoring interval thresholds for different health levels. The preliminary health score value is compared with the interval thresholds corresponding to each level in the health assessment standard to determine the specific scoring interval into which the preliminary health score value falls. The health status category of the target device is determined based on the health level definition corresponding to the scoring interval. At the same time, auxiliary judgment is made based on the equipment operating time, load utilization rate and historical health status change records. When the scoring critical value is close, the historical health decline trend of the equipment is preferentially referred to for judgment and correction. Finally, the current health status classification of the equipment is calibrated based on the comprehensive comparison results to determine the equipment health status information.

[0048] In one embodiment, if Figure 3As shown, in step S50, the trend prediction analysis is performed on the restored equipment operation status information through the equipment status trend prediction model to obtain the trend prediction analysis results, including: S501: Perform trend analysis on the restored equipment operating status information using an equipment status trend prediction model to extract health trend characteristic parameters.

[0049] Specifically, the operation status information of the equipment after recovery is arranged in chronological order to form a time series. The operation status data segments within the continuous time period are extracted based on the sliding time window. The equipment operation indicators in each segment are trend extracted using a time series analysis algorithm. The direction, magnitude and rate of change of the indicators over time are calculated. The direction of change is used to describe whether the equipment operation status shows an improvement or deterioration trend. The magnitude of change reflects the intensity of the change. The rate of change reflects the speed of the change process. Feature normalization processing is performed on the extracted trend elements. The trend characteristics of different indicators are uniformly converted into standard scale values, and the corresponding time window number and equipment identification information are marked to extract health trend feature parameters.

[0050] S502: Generate trend prediction analysis results based on health trend characteristic parameters.

[0051] Specifically, based on the extracted health trend characteristic parameters, each feature is classified and processed according to the set trend analysis model. The change direction parameter is used to determine whether the health status of the equipment is improving, stable or deteriorating. The change amplitude parameter is used to quantify the severity of the health status change. The change rate parameter is used to estimate the time urgency of the health status change. By assigning different analysis weights to various trend characteristic parameters, a comprehensive trend score value is calculated. The comprehensive trend score value is used to characterize the overall health change trend of the target equipment within the set evaluation time interval. At the same time, the comprehensive trend score value is fitted and corrected based on historical trend samples. During the correction process, the historical operation mode of the target equipment and the trend evolution path of similar equipment are referred to. Finally, the trend prediction category and change trend level of the target equipment within the set evaluation time interval are determined to generate trend prediction analysis results.

[0052] In one embodiment, if Figure 4 As shown, in step S501, the device status trend prediction model is used to perform trend analysis on the restored device operating status information to extract health trend characteristic parameters, including: S5011: Arrange the restored device operation status information in chronological order to form a time series of the restored device operation status.

[0053] Specifically, according to the timestamp attributes recorded in the equipment operation status information after recovery, the various operation parameter data are arranged in chronological order, the equipment number and corresponding collection time of each operation data are extracted, and a continuous data node chain is constructed by comparing the timestamp sequence. For data nodes with abnormal time intervals, the interval time is interpolated and padded to ensure that the data nodes on the time axis are evenly and continuously arranged. The original parameter values ​​are kept unchanged during the arrangement process, and are only sorted by time sequence. After the sorting is completed, the data nodes are grouped and archived according to the equipment number. Each group of archived data is stored independently according to a single device to form a time series of the equipment operation status after recovery.

[0054] S5012: Based on the time series of the equipment operating status after recovery, a sliding time window method is used to extract a subsequence of the equipment operating status after recovery within a preset time period.

[0055] Specifically, a sliding time window parameter is set for the formed time series of the operating status of the equipment after recovery, and the window length and the window sliding step are determined. According to the set time window length, the operating status data within a continuous time period is intercepted starting from the starting node of the time series, and the intercepted data fragment is recorded as a subsequence. At the same time, the time window is slid backward according to the set step length, and the data interception operation is repeated. During the sliding process, it is ensured that the subsequences are connected in a continuous time order. For the data missing sections in the time series, the linear interpolation method of the adjacent nodes is used to fill the data to ensure that the time span and data density of each extracted subsequence meet the preset standards. During the extraction process, each subsequence is marked with a number and stored in association with the corresponding equipment number to extract the subsequence of the operating status of the equipment after recovery.

[0056] S5013: Calculate the health trend change characteristics of the restored device operation status subsequence and extract the corresponding health trend characteristic parameters.

[0057] Specifically, for each extracted subsequence of the operating status of the equipment after recovery, continuous operating status parameter change data are extracted in order of time nodes, and the linear regression method is used to fit the parameter change trend in the subsequence, and the change direction, change amplitude and change rate of the operating status parameters on the time axis are calculated. The change direction is used to indicate whether the parameter shows an upward, downward or stable change trend with the increase of time. The change amplitude is used to quantify the overall amplitude of the parameter value change. The change rate is used to characterize the speed of the parameter value change per unit time. Each change feature is standardized according to the set numerical interval, and the standardized change direction feature, change amplitude feature and change rate feature are extracted. They are marked in combination with the start and end time of the subsequence and the equipment number to extract the corresponding health trend feature parameters.

[0058] In one embodiment, if Figure 5As shown, in step S60, based on the trend prediction analysis results, the health status change trend of the target device within the set evaluation time interval is determined, including: S601: Calculate the comprehensive trend change index of the target device within a set evaluation time interval based on the trend prediction analysis result.

[0059] Specifically, the health trend characteristic parameters corresponding to the target device in the trend prediction analysis results are extracted, and according to the preset trend comprehensive calculation rules, the change direction parameter, change amplitude parameter and change rate parameter are used as input variables. The change direction is mapped to a numerical interval using a qualitative assignment method, and the change amplitude and change rate are standardized to a unified numerical scale using a normalization processing method. The standardized trend characteristic parameters are weighted according to the set weight coefficient, and the contribution value of each type of trend feature to the health change trend of the device is calculated. All contribution values ​​are superimposed to form a trend feature comprehensive score value. At the same time, dynamic correction is performed based on the historical trend of the health status change of the device in the historical evaluation interval. Finally, a trend comprehensive change index is generated to characterize the overall trend of health changes of the target device in the set evaluation time interval, so as to calculate the trend comprehensive change index of the target device in the set evaluation time interval.

[0060] S602: Acquire device attribute information corresponding to the target device.

[0061] Specifically, based on the unique identification number of the target device, the corresponding device registration data is retrieved in the device basic information database, and the device category information, model information, factory time, recent maintenance record, installation environment parameters and operating environment category are extracted. The extracted information is archived and organized according to the set field classification method, where the device category information is used to indicate the category of the device in the functional classification system, the device model information is used to determine the reference standard of the performance parameters of the device, the factory time and maintenance records are used to assist in evaluating the device life cycle status, and the installation environment parameters and operating environment category are used to identify the physical environment characteristics of the device. The archived device attribute information is stored in a unified data structure and associated with the target device identification to obtain the device attribute information corresponding to the target device.

[0062] S603: Dynamically adjust trend determination information according to device attribute information.

[0063] Specifically, the device category information and operating environment parameter information contained in the device attribute information are extracted, and the corresponding basic trend judgment reference standard is determined based on the device category information. The temperature, humidity, altitude, environmental pollution level and electromagnetic interference intensity data in the operating environment parameter information are extracted, and differentiated comparisons are performed with the standard operating environment reference values ​​respectively. According to the comparison results, the environmental correction factor is calculated according to the sensitivity weight factor set for each environmental parameter. Based on the sign and numerical value of the environmental correction factor, the trend judgment direction and adjustment range that need to be adjusted are determined. For each health trend judgment parameter in the basic trend judgment reference standard, an upward or downward adjustment is performed according to the correction direction information. The adjustment range is weighted and calculated based on the sensitivity coefficient set for each parameter. After the adjustment is completed, the corrected trend judgment parameters are uniformly standardized and bound to the device category information and operating environment characteristics of the target device to dynamically adjust the trend judgment information.

[0064] S604: Based on the trend comprehensive change index and the trend determination information, determine the health status change trend of the target device within the set evaluation time interval.

[0065] Specifically, the calculated trend comprehensive change index is compared with the dynamically adjusted trend judgment information, and the numerical interval corresponding to the trend comprehensive change index is matched with the health trend level interval in the trend judgment information. If the trend comprehensive change index falls into the stable interval, the health status change trend of the target device within the set evaluation time interval is judged to be a stable state. If the trend comprehensive change index falls into the improvement interval, the health status of the target device is judged to be improving. If the trend comprehensive change index falls into the deterioration interval, the health status of the target device is judged to be deteriorating. For situations in the deterioration trend interval and close to the risk threshold, the historical trend change trajectory is further combined to perform trend weighted judgment to ensure the accuracy and consistency of the trend judgment results, so as to judge the health status change trend of the target device within the set evaluation time interval.

[0066] In one embodiment, if Figure 6 As shown, in step S603, the trend determination information is dynamically adjusted according to the device attribute information, including: S6031: Device attribute information includes device category information and operating environment parameter information.

[0067] In this embodiment, the device category information is the functional classification, technology type and application scenario information of the target device, and the operating environment parameter information is the temperature, humidity, altitude, environmental pollution level and electromagnetic interference level of the environment in which the target device is located.

[0068] Specifically, the basic attribute data of the equipment recorded in the equipment registration information table is extracted, and the equipment category information is used as the first component of the equipment attribute information. The equipment category information refers to the data content that characterizes the functional attributes and technical classification characteristics of the equipment in the power grid system, including the functional unit category, technical type code and typical application scenario description of the equipment. The environmental condition monitoring data corresponding to the equipment installation location is further read, and the read environmental condition data is used as the operating environment parameter information. The operating environment parameter information refers to the data set that characterizes the physical environment characteristics of the equipment, including temperature level, humidity level, altitude, environmental pollution level and electromagnetic interference intensity level. The extracted equipment category information and operating environment parameter information are stored in a standardized manner according to the set data format, and are associated and mapped with the unique identifier of the target equipment to determine the composition of the equipment attribute information.

[0069] S6032: Determine basic trend determination information corresponding to the target device based on the device category information.

[0070] Specifically, based on the extracted device category information, the preset device category trend judgment information database is retrieved, and the trend standard template that is consistent with the functional unit category and technical type code of the target device is matched. The normal range threshold and risk warning threshold corresponding to the health change direction, change amplitude and change rate are extracted from the trend standard template. If the device category information contains a description of a specific application scenario, some parameters in the basic trend judgment information are refined and adjusted according to the characteristics of the application scenario. When refining and adjusting, refer to the historical trend change samples of similar devices in similar application scenarios to ensure that the determined basic trend judgment information is consistent with the functional positioning and operating characteristics of the target device, so as to determine the basic trend judgment information corresponding to the target device.

[0071] S6033: Calculate the environmental correction factor based on the operating environment parameter information.

[0072] Specifically, the operating environment parameter information corresponding to the target device is extracted, and the real-time values ​​of temperature level, humidity level, altitude, environmental pollution level and electromagnetic interference intensity are read respectively. The values ​​of each environmental parameter are differentiated from the set standard environmental reference value. According to the sensitivity weight of each parameter on the potential impact of the equipment operation status, weighted processing is performed on the difference values. The weight factors of temperature level and humidity level are used to adjust the trend judgment information related to the stability of material performance. The weight factors of altitude and pollution level are used to adjust the trend judgment information related to the stability of electrical performance. The weight factor of electromagnetic interference intensity is used to adjust the trend judgment information related to communication and signal integrity. The environmental impact quantities after each weighted processing are superimposed and summed to form a unified environmental correction factor to calculate the environmental correction factor.

[0073] S6034: Fusion of basic trend determination information and environmental correction factors to generate trend determination information.

[0074] Specifically, the various health trend determination threshold parameters in the basic trend determination information corresponding to the target device are extracted, and the direction and correction amplitude information of each threshold parameter that needs to be corrected are determined based on the calculated environmental correction factor. For the case where the environmental correction factor is a positive value, the basic threshold range is adjusted upward according to the correction amplitude information. For the case where the environmental correction factor is a negative value, the basic threshold range is adjusted downward according to the correction amplitude information. During the correction process, the sensitivity coefficient originally set for each threshold parameter is used as a reference for the correction ratio to avoid excessive deviation of the trend standard due to a single environmental factor. After the correction is completed, the updated threshold parameters are re-normalized and bound to the device category information and operating environment parameter information to form trend determination information that is adapted to the actual conditions of the current device operating environment to generate trend determination information.

[0075] In one embodiment, if Figure 7 As shown, in step S6034, the basic trend determination information is integrated with the environmental correction factor to generate trend determination information, including: S60341: Based on the environmental correction factor, determine the correction direction information and correction amplitude information of the basic trend judgment information.

[0076] Specifically, the correction direction information and correction amplitude information are determined based on the sign information and numerical value of the calculated environmental correction factor. If the environmental correction factor is a positive value, it is determined that the relevant health trend threshold in the basic trend determination information needs to be adjusted upward. If the environmental correction factor is a negative value, it is determined that the relevant health trend threshold in the basic trend determination information needs to be adjusted downward. The absolute value of the environmental correction factor is extracted as the basic reference amount for the correction amplitude information. According to the sensitivity coefficient originally set for each health trend determination threshold, the correction amplitude information is weightedly adjusted. The correction amplitude information of the threshold item with a higher sensitivity coefficient is appropriately amplified, and the correction amplitude information of the threshold item with a lower sensitivity coefficient is appropriately reduced. Finally, the specific correction direction information and correction amplitude information corresponding to each basic trend determination information parameter are formed to determine the correction direction information and correction amplitude information of the basic trend determination information.

[0077] S60342: Perform correction processing on the basic trend determination information according to the correction direction information and the correction amplitude information to obtain trend determination information.

[0078] Specifically, for each basic trend judgment information parameter, the adjustment method is determined according to the corresponding correction direction information. If the correction direction information is an upward adjustment, the correction amplitude information is added on the basis of the original threshold. If the correction direction information is a downward adjustment, the correction amplitude information is reduced on the basis of the original threshold. During the adjustment process, the original logical relationship of each parameter is kept consistent to ensure that the division order of each health trend level interval is not misplaced. When performing the correction processing, a minimum adjustment limit is set for the high-risk warning threshold to avoid the trend judgment information being invalid due to excessive correction amplitude information. After the adjustment is completed, all corrected trend parameters are re-normalized to ensure that each trend parameter is within a unified standard scale, and is associated and bound with the device category information and operating environment parameter information of the target device to obtain trend judgment information.

[0079] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0080] In one embodiment, a power grid state visualization and operation and maintenance management device is provided, and the power grid state visualization and operation and maintenance management device corresponds one-to-one with the power grid state visualization and operation and maintenance management method in the above embodiment. Figure 8 As shown, the power grid status visualization and operation and maintenance management device includes an operation status acquisition module, an equipment status analysis module, a status information display module, an equipment abnormality processing module, a trend prediction and analysis module, a risk assessment and task generation module, and an operation and maintenance task scheduling module. The functional modules are described in detail as follows: Operation status acquisition module, used to obtain operation status information of power equipment in the power grid; The equipment status analysis module is used to input the operating status information into the equipment status analysis model to obtain the equipment health status information; Status information display module, used to display equipment health status information in the power grid status display interface; The device abnormality processing module is used to generate device repair information if the device health status information is abnormal, repair the target device based on the device repair information, and obtain the device operating status information after recovery; The trend prediction analysis module is used to perform trend prediction analysis on the operation status information of the restored equipment through the equipment status trend prediction model to obtain trend prediction analysis results; The risk assessment and task generation module is used to determine the health status change trend of the target device within the set assessment time interval based on the trend prediction analysis results. If the health status change trend indicates that the target device is at risk, predictive operation and maintenance task information is generated; The operation and maintenance task scheduling module is used to generate task scheduling information based on predictive operation and maintenance task information, combined with the real-time location information, task priority, resource usage and response rules of the operation and maintenance personnel.

[0081] Optionally, the device status analysis module includes: The feature extraction processing submodule is used to perform feature extraction processing on the received operating status information through the equipment status analysis model, and extract the equipment operating status indicator information from the operating status information; The indicator standardization processing submodule is used for the equipment status analysis model to perform standardization processing on the equipment operation status indicator information based on the preset feature weight factor to obtain the standardized equipment operation status indicator information; The weighted calculation processing submodule is used for the equipment status analysis model to perform weighted summation processing on the standardized equipment operation status indicator information based on the feature weight ratio corresponding to each indicator to obtain a preliminary health score value; The health status assessment submodule is used to compare the preliminary health score value with the health assessment standard corresponding to the device category to determine the device health status information.

[0082] Optional, trend forecast analysis module includes; The trend feature extraction submodule is used to perform trend analysis on the equipment operation status information after recovery through the equipment status trend prediction model and extract health trend feature parameters; The trend prediction generation submodule is used to generate trend prediction analysis results based on health trend characteristic parameters.

[0083] Optionally, the trend feature extraction submodule includes: The time series construction submodule is used to arrange the operation status information of the equipment after recovery in chronological order to form a time series of the operation status of the equipment after recovery; The subsequence extraction submodule is used to extract the subsequence of the restored equipment operating status within a preset time period using a sliding time window method based on the time series of the restored equipment operating status; The trend feature calculation submodule is used to calculate the health trend change characteristics of the equipment operation status subsequence after recovery and extract the corresponding health trend feature parameters.

[0084] Optional risk assessment and task generation modules include: The trend index calculation submodule is used to calculate the comprehensive trend change index of the target device within the set evaluation time interval based on the trend prediction analysis results; The device attribute extraction submodule is used to obtain the device attribute information corresponding to the target device; The trend standard adjustment submodule is used to dynamically adjust the trend determination information according to the device attribute information; The trend change determination submodule is used to determine the health status change trend of the target device within a set evaluation time interval based on the trend comprehensive change index and trend determination information.

[0085] Optionally, the trend standard adjustment submodule includes: Attribute information building unit, used for device attribute information including device category information and operating environment parameter information; A basic trend standard determination unit is used to determine basic trend determination information corresponding to the target device according to the device category information; An environmental correction factor calculation unit, used to calculate the environmental correction factor based on the operating environment parameter information; The trend standard fusion generation unit is used to fuse the basic trend determination information with the environmental correction factor to generate trend determination information.

[0086] Optionally, the trend standard fusion generation unit includes: A trend correction parameter determination subunit is used to determine correction direction information and correction amplitude information of the basic trend determination information based on the environmental correction factor; The trend standard correction processing subunit is used to perform correction processing on the basic trend determination information according to the correction direction information and the correction amplitude information to obtain trend determination information.

[0087] The specific definition of a power grid status visualization and operation and maintenance management device can be found in the definition of a power grid status visualization and operation and maintenance management method described above and will not be further elaborated here. Each module in the aforementioned ... device may be implemented in whole or in part via software, hardware, or a combination thereof. Each of the aforementioned modules may be embedded in or independent of a processor in a computer device in hardware form, or may be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each of the aforementioned modules.

[0088] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements a method for visualizing and managing power grid status.

[0089] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: Obtain operating status information of power equipment in the power grid; Input the operating status information into the equipment status analysis model to obtain the equipment health status information; Display equipment health status information in the power grid status display interface; If the device health status information is abnormal, device repair information is generated, and based on the device repair information, the target device is repaired to obtain the device operating status information after recovery; The equipment status trend prediction model is used to perform trend prediction analysis on the equipment operation status information after recovery to obtain trend prediction analysis results; Based on the trend prediction analysis results, the health status change trend of the target device within the set evaluation time interval is determined. If the health status change trend indicates that the target device is at risk, predictive operation and maintenance task information is generated; Based on the predictive operation and maintenance task information, combined with the real-time location information of the operation and maintenance personnel, task priority, resource occupancy and response rules, task scheduling information is generated.

[0090] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain operating status information of power equipment in the power grid; Input the operating status information into the equipment status analysis model to obtain the equipment health status information; Display equipment health status information in the power grid status display interface; If the device health status information is abnormal, device repair information is generated, and based on the device repair information, the target device is repaired to obtain the device operating status information after recovery; The equipment status trend prediction model is used to perform trend prediction analysis on the equipment operation status information after recovery to obtain trend prediction analysis results; Based on the trend prediction analysis results, the health status change trend of the target device within the set evaluation time interval is determined. If the health status change trend indicates that the target device is at risk, predictive operation and maintenance task information is generated; Based on the predictive operation and maintenance task information, combined with the real-time location information of the operation and maintenance personnel, task priority, resource occupancy and response rules, task scheduling information is generated.

[0091] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0092] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0093] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for visualizing and managing power grid status, characterized in that: The method for visualizing and managing power grid status includes: Obtain operating status information of power equipment in the power grid; Inputting the operating status information into the equipment status analysis model to obtain equipment health status information; Displaying the health status information of the device in the power grid status display interface; If the device health status information is abnormal, device repair information is generated, and the target device is repaired based on the device repair information to obtain restored device operation status information; Performing trend prediction analysis on the post-recovery equipment operation status information using an equipment status trend prediction model to obtain a trend prediction analysis result; Based on the trend prediction analysis results, determining the health status change trend of the target device within a set evaluation time interval, and generating predictive operation and maintenance task information if the health status change trend indicates that the target device is at risk; Task scheduling information is generated based on the predictive operation and maintenance task information, combined with the real-time location information, task priority, resource occupancy, and response rules of the operation and maintenance personnel.

2. A method for visualizing and managing power grid status according to claim 1, characterized in that: Inputting the operating status information into the device status analysis model to obtain device health status information includes: Performing feature extraction processing on the received operating status information through the device status analysis model to extract device operating status indicator information from the operating status information; The device status analysis model performs normalization processing on the device operation status indicator information based on a preset feature weight factor to obtain normalized device operation status indicator information; The equipment status analysis model performs weighted summation processing on the standardized equipment operation status indicator information based on the characteristic weight ratio corresponding to each indicator to obtain a preliminary health score value; The preliminary health score value is compared with the health assessment standard corresponding to the device category to determine the device health status information.

3. A method for visualizing and managing power grid status according to claim 1, characterized in that: The performing trend prediction analysis on the post-recovery equipment operation status information by the equipment status trend prediction model to obtain trend prediction analysis results includes: Performing trend analysis on the post-recovery equipment operating status information through an equipment status trend prediction model to extract health trend characteristic parameters; Based on the health trend characteristic parameters, a trend prediction analysis result is generated.

4. A method for visualizing and managing power grid status according to claim 3, characterized in that: The device status trend prediction model is used to perform trend analysis on the restored device operating status information to extract health trend characteristic parameters, including: Arranging the restored device operating status information in chronological order to form a restored device operating status time series; Based on the time series of the equipment operating status after recovery, a sliding time window method is used to extract a subsequence of the equipment operating status after recovery within a preset time period; For the post-recovery device operating state subsequence, health trend change characteristics are calculated and corresponding health trend characteristic parameters are extracted.

5. A method for visualizing and managing power grid status according to claim 1, characterized in that: The step of determining a health status change trend of the target device within a set evaluation time interval based on the trend prediction analysis result includes: Calculating a comprehensive trend change index of the target device within the set evaluation time interval based on the trend prediction analysis result; Acquire device attribute information corresponding to the target device; Dynamically adjusting trend determination information based on the device attribute information; The health status change trend of the target device within the set evaluation time interval is determined based on the trend comprehensive change index and the trend determination information.

6. A method for visualizing and operating a power grid according to claim 5, characterized in that: The dynamically adjusting trend determination information according to the device attribute information includes: The device attribute information includes device category information and operating environment parameter information; Determining basic trend determination information corresponding to the target device according to the device category information; Calculating an environmental correction factor based on the operating environment parameter information; The basic trend determination information is fused with the environmental correction factor to generate the trend determination information.

7. A method for visualizing and operating a power grid according to claim 6, characterized in that: The step of fusing the basic trend determination information with the environmental correction factor to generate the trend determination information includes: Determining correction direction information and correction amplitude information for the basic trend determination information based on the environmental correction factor; According to the correction direction information and the correction amplitude information, correction processing is performed on the basic trend determination information to obtain the trend determination information.

8. A power grid status visualization and operation and maintenance management device, characterized in that: The power grid status visualization and operation and maintenance management device includes: Operation status acquisition module, used to obtain operation status information of power equipment in the power grid; An equipment status analysis module is used to input the operating status information into an equipment status analysis model to obtain equipment health status information; A status information display module is used to display the health status information of the device in the power grid status display interface; an equipment abnormality processing module, configured to generate equipment repair information if the equipment health status information is abnormal, repair the target equipment based on the equipment repair information, and obtain equipment operating status information after recovery; A trend prediction analysis module is used to perform trend prediction analysis on the post-recovery equipment operation status information using an equipment status trend prediction model to obtain a trend prediction analysis result; a risk assessment and task generation module, configured to determine, based on the trend prediction analysis results, the health status change trend of the target device within a set assessment time interval, and generate predictive operation and maintenance task information if the health status change trend indicates that the target device is at risk; The operation and maintenance task scheduling module is used to generate task scheduling information based on the predictive operation and maintenance task information, combined with the real-time location information, task priority, resource occupancy and response rules of the operation and maintenance personnel.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the power grid status visualization and operation and maintenance management method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power grid status visualization and operation and maintenance management method as claimed in any one of claims 1 to 7 are implemented.

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